fit finder
The Size Advisor Tool that Solves E-Tail’s Fit Inconsistency Problem
Having a sizing solution is a requirement for online retailers - customers expect their garments to have a perfect first-time fit. This size expectation applies to established brands and new players alike. It is extremely important for brands to provide accurate sizing information for their shoppers - and the ability to give correct fit information goes way beyond insufficient and unreliable size charts.
Finding an adequate way to solve the size conundrum continues to be one of retail’s problems. When asked about the relationship between the perfect fit and a customer’s shopping journey,
, Director of footwear brand, Pelle, responded that “today’s market is constantly evolving, and consumers want a tailored experience to allow them to buy with confidence.”
When Fit Analytics launched in 2010, data was at its core. At the time, sizing solutions were a new idea, so there was not a lot of data-driven information out there.
According to
“when online fit technologies first appeared, they were hyped as the answer to retailers’ prayers. But the reality was very different – the technology was too clunky or did not work well, leaving consumers and retailers skeptical.”
Over the years, technology has evolved, leading to innovative fit solutions in the retail industry.
, Principal Fashion Analyst at Kantar Consulting lists 3D-body scanning or imaging and micro-measurement technology, as some of the interesting fast-developing innovations.
Augmented reality and virtual mannequins can also be added to the list.
Initially, Fit Analytics developed proprietary technologies based on hundreds of thousands of 3D body scans, millions of answers to body modeling questions, and billions of purchases and returns records.
In 2013, Fit Analytics introduced a machine-learning approach to its algorithms. This gave birth to its sizing tool,
. When asked to describe the purpose of the online size advisor, CEO, Sebastian Schulze explained,
“Standard size charts never did, and still don’t, offer the same accurate size advice as data and machine learning. After years in the industry, it was clear we could take our technologies a step further.”
solves one of the problems an online retailer faces by offering shoppers a user-friendly way of finding the perfect fit when they shop online. With this tool, retailers can rely on the crucial data behind the size advisor’s recommendations. The reason being, the generated size recommendation is a combination of aggregate inputs and preferences given by customers, multiple data sources, and social proof.